Speckle Noise Reduction Research Articles

Speckle noise reduction for ultrasound images by using speckle reducing anisotropic diffusion and Bayes threshold.

Ultrasound imaging has been used for diagnosing lesions in the human body. In the process of acquiring ultrasound images, speckle noise may occur, affecting image quality and auto-lesion classification. Despite the efforts to resolve this, conventional algorithms exhibit poor speckle noise removal and edge preservation performance. Accordingly, in this study, a novel algorithm is proposed based on speckle reducing anisotropic diffusion (SRAD) and a Bayes threshold in the wavelet domain. In this algorithm, SRAD is employed as a preprocessing filter, and the Bayes threshold is used to remove the residual noise in the resulting image. Compared to the conventional filtering techniques, experimental results showed that the proposed algorithm exhibited superior performance in terms of peak signal-to-noise ratio (average = 28.61 dB) and structural similarity (average = 0.778).

A Hybrid Neural Filter (HNF) Based on Adaptive Median and Weiner Techniques for Reducing Speckle Noise of Ultrasound Liver Tumor Images

Pre-processing of image is considered to be an important aspect medical image analysis in order to enhance its quality. Noise reduction is performed for improvement in the visual quality and removing redundant image values. Ways of pre-processing of image are a necessity, for removing the noise and for quality enhancement of the image. Before applying any approach on medical images, measures used to pre-process seem to be vital for limiting the abnormalities’ findings with no influence from background of the medical image. In this work, a filter is proposed based on Adaptive Median and Weiner Hybrid Neural Filter for noise reduction. The review of filtering techniques is implemented using MATLAB platform, followed by comparison of results with different filtering techniques to show the system effectiveness.

Hybrid method for speckle noise reduction in digital holography.

In digital holography, the inherited speckle noise degrades imaging quality due to the coherent laser source. To overcome this problem, a hybrid method for speckle noise reduction is presented by combining a novel angular diversity approach with the block-matching and 3D filtering (BM3D) algorithm. A serial of holograms is first captured by the proposed recording approach, and then the image with high signal-to-noise ratio is obtained by averaging multiple reconstructed intensity images. Finally, the residual noise in the averaged image is further eliminated by the BM3D filtering algorithm. The speckle noise is significantly suppressed, and a nearly speckle-free image can be obtained. Experimental results demonstrate the effectiveness of the proposed method.

Optical Energy Variability Induced by Speckle: The Cases of MERLIN and CHARM-F IPDA Lidar

In the context of the FrenchGerman space lidar mission MERLIN (MEthane Remote LIdar missioN) dedicated to the determination of the atmospheric methane content, an end-to-end mission simulator is being developed. In order to check whether the instrument design meets the performance requirements, simulations have to count all the sources of noise on the measurements like the optical energy variability induced by speckle. Speckle is due to interference as the lidar beam is quasi monochromatic. Speckle contribution to the error budget has to be estimated but also simulated. In this paper, the speckle theory is revisited and applied to MERLIN lidar and also to the DLR (Deutsches Zentrum für Luft und Raumfahrt) demonstrator lidar CHARM-F. Results show: on the signal path, speckle noise depends mainly on the size of the illuminated area on ground; on the solar flux, speckle is fully negligible both because of the pixel size and the optical filter spectral width; on the energy monitoring path a decorrelation mechanism is needed to reduce speckle noise on averaged data. Speckle noises for MERLIN and CHARM-F can be simulated by Gaussian noises with only one random draw by shot separately for energy monitoring and signal paths.

Speckle denoising by variant nonlocal means methods.

This study aims to demonstrate the performances of nonlocal means (NLM) and their variant denoising methods, mainly focusing on NLM-shaped adaptive patches and several NLM-reprojection schemes for speckle noise reduction in amplitude and phase images of the digital coherent imaging systems. In the digital coherent imaging systems such as digital speckle pattern interferometry, digital holographic interferometry, etc., the image quality is severely degraded by additive uncorrelated speckle noise, due to the coherent nature of the light source, and therefore limits the development of several applications of these imaging systems in many fields. NLM and its variant denoising methods are employed to denoise the intensity/phase images obtained from these imaging systems, and their effectiveness is evaluated by considering various parameters. The performance comparison of these methods with other existing speckle denoising methods is also presented. The performance of these methods for speckle noise reduction is quantified on the basis of two criteria matrices, namely, the peak-to-signal noise ratio and the image quality index. Based on these criteria matrices, it is observed that these denoising methods have the ability to improve the intensity and phase images favorably in comparison to other speckle denoising techniques, and these methods are more effective and feasible in speckle-noise reduction.

14.2: Reduction of speckle noise by spatial‐multiplexing pixel separation in computer generated holographic display

To reduce the speckle noise in a holographic display, a method by separating the pixel or object points of reconstructed images is proposed using a light source array. The multiple sparse images are reconstructed and shifted together to form the complete images with the speckleless noise. The simulation results show that the speckle noise is reduced from 51% to 21% by using a 3 × 3 light source array. It is expected that it could be used for realizing a dynamical 3D holographic display with the less speckle noise in the future.

Improved Adaptive Wavelet Thresholding for Effective Speckle Noise Reduction in Low Contrast Medical Images

Informative images endure from poor contrast and noise during image acquisition. Significant information retrieval necessitates image contrast enhancement and removal of noise as a prerequisite before any further processing can be done. Dominant applications with low contrast images affected by speckle noise are medical ultrasound images. The objective of this work is to improve the effectiveness of the preprocessing stage in medical ultrasound images by enhancing the image while retaining its structural characteristics. For image enhancement, this work proposes to develop an automatic contrast enhancement technique using cumulative histogram equalization and gamma correction based on the image. For noise removal, this work proposes an algorithm Gamma Correction with Exponentially Adaptive Threshold (GCEAT) which suggests the use of GC for contrast enhancement along with a new wavelet-based adaptive soft thresholding technique for noise removal. The proposed GCEAT-based image de-noising is validated with other enhancement and noise removal techniques. Experimental results with low contrast synthetic and actual ultrasound images show that the suggested proposed system performs better than existing contrast enhancement techniques. Encouraging results were obtained with medical ultrasound images in terms of Peak-Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measure (SSIM) and Average Intensity (AI).

DN-GAN: Denoising generative adversarial networks for speckle noise reduction in optical coherence tomography images

Optical coherence tomography (OCT) is an efficient noninvasive bioimaging technique that can measure retinal tissue. Considering the changes in the acquisition environment during imaging, the OCT images are affected by granular speckle noise, thereby reducing the image quality. In this paper, an efficient method based on generative adversarial network is proposed to reduce the speckle noise and preserve the texture details. The proposed model consists of two components, that is, a denoising generator and a discriminator. The denoising generator learns how to map the noise image to the ground truth. The discriminator learns as a loss function to compare the differences between the ground truth and the image reconstructed by the generator. A number of repeated densely sampled B-scan OCT images are used with multi-frame registration to train the denoising generator. The original OCT images are denoised by a trained generator to quickly and efficiently obtain improved quality. Results showed that the proposed method outperforms the other popular methods, and achieves a better denoising effectiveness.

An Optimized Hybrid Filter designing for Speckle and Gaussian Noise Reduction in Ultra-Sound Images

Image which is a visual perception of a scene or something is a set of pixels that have certain values which appear in the form of colors to view that particular set of pixel as image. Image contains information about whatever is being depicted in the picture and hence it can be said as a useful source for storing or conveying information. Image noise is apparent in image region with low signal level, such as shadow region or under exposed images. The work presents the concept of noising and denoising in a digital image. Noise is a kind of disturbance that occurs in the channel at the time of transmission. Image denoising is the procedure of improving true images from the noisy images. For ages researchers have been proposing several techniques that were used to remove the noise from the image. Image denoising is the procedure of improving true image from the noisy image. At the time of such process it is difficult to reduce noise. Owing to this difficulty, numerous denoising models have been proposed [4]. The paper presents the reduction of speckle and Gaussian noise in the biomedical ultrasound images. In the proposed work, the Butterworth filter is applied for filtering the noisy image and then the coefficients are optimized by using the firefly algorithm mechanism to remove noise that occurs at the time of transmission and then it is hybrid with the Weiner filter. Experiments have been performed to check the performance of the proposed technique. The results are analyzed quantatively using PSNR and SSIM. The results are also evaluated on other performance parameters such as BER, MSE and fitness on speckle as well as on Gaussian noise.

Color LED DMD holographic display with high resolution across large depth.

Holographic displays employing digital micromirror devices (DMDs) reconstruct 3D images at high diffraction orders. For LED displays, this geometry introduces large dispersion at the DMD surface, reducing image resolution and depth. This work proposes a color DMD LED holographic display with dispersion compensation utilizing an additional diffraction grating in an illumination module. The solution allows to obtain image depth up to 100mm, which is comparable to the one achieved by a liquid crystal spatial light modulator, where the limiting factor is spatial coherence of the source. Experimental comparison of the results obtained with the laser and LED source gives evidence of effective speckle noise reduction, even from a single frame.

Classification for liver ultrasound tomography by posterior attenuation correction with a phantom study.

The B-mode ultrasound usually contains scattering speckle noise which reduces the detailed resolution of the target and is regarded as an intrinsic noise that interferes with diagnostic precision. The aim of this study was to classify hepatic steatosis through applying attenuation correction with a phantom to reduce speckle noise in liver ultrasound tomography in patients. This retrospective study applied three randomized groups signifying different liver statuses. A total of 114 patients' effective liver ultrasound images-30 normal, 44 fatty, and 40 cancerous-were included. The proposed depth attenuation correction method was first applied to images. Three regions of interest were manually drawn on the images. Next, five feature values for the regions of interest were calculated. Finally, the hybrid method of logistic regression and support vector machine was employed to classify the ultrasound images with 10-fold cross-validation. The accuracy, kappa statistic, and mean absolute error of the proposed hybrid method were 87.5%, 0.812, and 0.119, respectively, which were higher than those of the logistic regression method-75.0%, 0.548, and 0.280-or those of the support vector machine method-75.7%, 0.637, and 0.293-respectively. Therefore, the hybrid method has been proven to be more accurate and have better performance and less error than either single method. The hybrid method provided acceptable accuracy of classification in three liver ultrasound image groups after depth attenuation correction. In the future, the deep learning approaches may be considered for the application in classifying liver ultrasound images.

BM3D-based total variation algorithm for speckle removal with structure-preserving in OCT images.

In this paper, we propose a total variation based on block matching 3D (BM3D-TV method), which includes the total variation regular term, the data fidelity term, and the block matching term. In addition, we also propose a fast numerical algorithm based on the split Bregman iteration for the proposed method. By assigning suitable weights to the data fidelity term and block matching term, the image noise reduction and the image structural characteristics can be matched optimally. We test the proposed method on six human retinal and one mouse skin optical coherence tomography (OCT) images respectively, and also compare it with total variation (TV) and BM3D, which were proved to be effective in denoising. The performances of these methods are quantitatively evaluated in terms of the signal-to-noise ratio, the contrast-to-noise ratio, and the averaged equivalent number of homogeneous areas at the aspects of speckle reduction and structure protection. Vast experiments show that the BM3D-TV method can effectively reduce speckle noise in OCT images, protect important structural information and improve image quality, compared with the BM3D and TV methods.

The Beltrami SAR Framework for Multichannel Despeckling

In this paper, a new framework for iterative speckle noise reduction in polarimetric synthetic aperture radar (SAR) data is introduced. Speckle is inherent to all coherent imaging systems and affects SAR imagery in the form of strong intensity variations in pixels with similar backscattering coefficients. This makes the interpretation of SAR data in several applications a difficult task. The proposed framework includes a preprocessing step capable of dealing with noise correlation usually found in single-look data. The general filtering approach is based on the Beltrami flow for denoising manifolds or images painted on manifolds. The principal contribution of this work is to adapt this approach to deal with covariance or coherency matrices instead of optical imagery. The evaluation presented suggests that this approach allows for good spatial and radiometric preservation compared to other state-of-the-art methods. Experiments are performed on the basis of synthetic and real-world experimental data. The validation of the proposed framework is accomplished using two refined error performance measures and the well-known effective number of looks measured. The source code of a parallel implementation of the proposed framework is released under the MPL 2.0 ( https://www.mozilla.org/en-US/MPL/2.0/ ) alongside this paper.

Optimization of Mammary Tissue Displacement in Ultrasound Elastography

The displacement of mammary tissues in static ultrasound elastography is often contaminated by the speckle noise deteriorating its quality. Several techniques have been developed in this context, in order to treat the noise present in images of breast tissue displacement, the progress of research work in noise processing is always questioned, especially that must be taken into account the trade-off between noise reduction and preservation of breast tissue texture. In this paper, a new strategy has been proposed to reduce speckle noise. The proposed method not only filters the image against noise, but also preserves the details and contours of the tissue texture. The approach developed is based on the coupling between the image reconstructions by filtered back projection (RPF) with an adaptive filter. The proposed model proposed has been validated on an in-vivo database comprising 20 images of the breast tissues displacement. Qualitative and quantitative improvements were noted. By comparing the proposed method with the wavelet technique, we show that it is more efficient in terms of calculating the standard deviation between the pixels (SD), it is better in terms of calculation of the Contrast / Noise ratio (CNR). And is much faster than the wavelet technique. The results of the proposed model are encouraging, and the chosen method is ready to be used in the improvement of images of mammary tissue displacements in ultrasound elastography.

Feature Enhancement in Medical Ultrasound Videos Using Contrast-Limited Adaptive Histogram Equalization.

Speckle noise reduction algorithms are extensively used in the field of ultrasound image analysis with the aim of improving image quality and diagnostic accuracy. However, significant speckle filtering induces blurring, and this requires the enhancement of features and fine details. We propose a novel framework for both multiplicative noise suppression and robust contrast enhancement and demonstrate its effectiveness using a wide range of clinical ultrasound scans. Our approach to noise suppression uses a novel algorithm based on a convolutional neural network that is first trained on synthetically modeled ultrasound images and then applied on real ultrasound videos. The feature improvement stage uses an improved contrast-limited adaptive histogram equalization (CLAHE) method for enhancing texture features, contrast, resolvable details, and image structures to which the human visual system is sensitive in ultrasound video frames. The proposed CLAHE algorithm also considers an automatic system for evaluating the grid size using entropy, and three different target distribution functions (uniform, Rayleigh, and exponential), and interpolation techniques (B-spline, cubic, and Lanczos-3). An extensive comparative study has been performed to find the most suitable distribution and interpolation techniques and also the optimal clip limit for ultrasound video feature enhancement after speckle suppression. Subjective assessments by four radiologists and experimental validation using three quality metrics clearly indicate that the proposed framework generates superior performance compared with other well-established methods. The processing pipeline reduces speckle effectively while preserving essential information and enhancing the overall visual quality and therefore could find immediate applications in real-time ultrasound video segmentation and classification algorithms.

Validation of Sea Surface Wind From Sentinel-1A/B SAR Data in the Coastal Regions of the Korean Peninsula

In this study, using in situ measurements at 17 buoy stations off the Korean Peninsula, C-band model (CMOD) functions for Sentinel-1A/B IW mode synthetic aperture radar (SAR) data were validated. In total, 395 Sentinel-1A/B IW mode dual-vertical polarized images were used for collocation with in situ measurements from May 1, 2015, to September 30, 2017, and 807 matchup points were obtained. Prior to the validation, preprocessing such as speckle noise reduction and ship and land masking was completed. The in situ wind speeds were converted to a 10-m neutral wind considering atmospheric stability. High-resolution wind speeds were estimated by using the CMOD functions such as CMOD4, CMOD_IFR2, CMOD5, CMOD5.N, and CMOD5.Na. The root-mean-square errors of each model were less than approximately 1.8 m·s–1 (1.83, 1.82, 1.69, 1.68, and 1.65 m·s–1, respectively). The biases of all models were higher in the western coastal region than those in the eastern coastal region. The results showed the advantages and disadvantages of each model in the estimation of wind speeds in the coastal region around the Korean Peninsula as proposed in a concept of combined errors. The wind speeds derived from the SAR data also presented a tendency for water depth to be overestimated over shallow bathymetry and to be underestimated at high wind speeds. In addition, this study assessed potential sources of wind speed errors such as the effects originating from wind direction input, different platforms of Sentinel-1A and Sentinel-1B and their calibration, and from radar interference or regional oceanic characteristic environments.

Speckle Reduction in Optical Coherence Tomography via Super-Resolution Reconstruction.

Reducing speckle noise from the optical coherence tomograms (OCT) of human retina is a fundamental step to a better visualization and analysis in retinal imaging, as thus to support examination, diagnosis and treatment of many eye diseases. In this study, we propose a new method for speckle reduction in OCT images using the super-resolution technology. It merges multiple images for the same scene but with sub-pixel movements and restores the missing signals in one pixel, which significantly improves the image quality. The proposed method is evaluated on a dataset of 20 OCT volumes (5120 images), through the mean square error, peak signal to noise ratio and the mean structure similarity index using high quality line-scan images as reference. The experimental results show that the proposed method outperforms existing state-of-the-art approaches in applicability, effectiveness, and accuracy.

Automatic femur length measurement for fetal ultrasound image using localizing region-based active contour method

This research aims to apply the localizing region-based active contour (LRAC) method to acquire the femur length in an ultrasound image automatically and to determine the effect of noise removal on the segmentation accuracy. The automatic femur length measurement system includes three main steps. The first step is the denoising process to reduce speckle noise in the ultrasound image. Afterwards, the LRAC method is applied to detect and segment a local region. The segmentation process with a certain number of iterations and a weight of the smoothing terms is started at the selected initial pixel. At the final step, the femur length is measured to estimate the gestational age. The experiment results show that the accuracy of the estimated gestational age increases significantly when the noise reduction technique is employed.

Speckle Noise Filtering in Side-Scan Sonar Images Based on the Tucker Tensor Decomposition.

Real signals are usually contaminated with various types of noise. This phenomenon has a negative impact on the operation of systems that rely on signals processing. In this paper, we propose a tensor-based method for speckle noise reduction in the side-scan sonar images. The method is based on the Tucker decomposition with automatically determined ranks of factoring tensors. As verified experimentally, the proposed method shows very good results, outperforming other types of speckle-noise filters.

Fast GPU-Based Enhanced Wiener Filter for Despeckling SAR Data

Speckle noise is presented as an inherent dilemma that affects the image processing field, and in particular synthetic aperture radar images. In order to mitigate the adverse effects caused by this phenomenon, several approaches have been introduced in the scientific community during the last three decades including spatial-based and non-local filtering approaches. However, these proposed techniques suffer from some limitations. In fact, it is very difficult to find an approach that is able, on the one hand, to perform well in terms of noise reduction and image detail preservation and, on the other hand, provide a filtering output solution without high computational complexity and within a short processing time. In this paper, we aim to evaluate the performance of a newly-developed despeckling algorithm, presented as an enhancement of the classical Wiener filter and properly designed to work with a Graphics Processing Unit (GPU). The algorithm is tested on both a simulated framework and real Sentinel-1 SAR data. The results, obtained in comparison with other filters, are interesting and promising. Indeed, the proposed method turns out to be a useful filtering instrument in the case of large images by performing the processing within a limited time and ensuring good speckle noise reduction with a considerable image detail preservation.